Using Data Analytics to Improve Order Fulfillment

Most logistics consultants skip this step when optimizing small parcel services. It's the reason your ops are stuck at 80% efficiency.👇 Here's the truth: data is king in logistics optimization. But not just any data. The right data. The step most consultants miss? Comprehensive carrier performance analysis. They focus on rates, but ignore: - Actual transit times vs. promised - Damage rates by route and carrier - Exception handling efficiency - Claims resolution speed Without this intel, you're flying blind. Your optimization efforts hit a ceiling. You can't improve what you don't measure. How to fix it: 1. Implement detailed tracking for every shipment 2. Analyze patterns over 3-6 months 3. Identify weak points in your carrier mix 4. Negotiate based on real performance, not just rates 5. Continuously monitor and adjust Result? Happier customers, lower costs, smoother operations. The difference between good and great logistics is hidden in the details most overlook. Master these details, and watch your logistics transform. Optimize smarter, not harder. #LogisticsOptimization #DataDriven #CarrierPerformance #EfficiencyBoost #SupplyChainManagement #ParcelDelivery #OperationalExcellence #PerformanceAnalysis #ShipmentTracking #ContinuousImprovement

Parcel End to End Visibility - Who, What, Where, When, Why... and How? WHO - Warehouse/Fulfillment & Parcel Transportation Carriers WHAT - Key milestones from "click to delivery" WHERE - YOUR web portal, email, text WHEN - Now WHY - Internal benefits - Own the customer experience, Identify bottlenecks, Accountability External benefits - Proactive communication = customer satisfaction HOW: 1. Identify data source for "key milestones" from "click to delivery (EXAMPLE - Order received, Picking order, Packing order, Shipping order, Carrier first scan, Carrier exception scans, Carrier out for delivery, Carrier Delivered, Carrier Picture POD) 2. Structure and normalize data 3. Create "INTERNAL" and "EXTERNAL" views and normalized terminology (EXAMPLE - Internal view will include every scan during fulfillment process and carriers while external view will normalize language of fewer key milestones - Order received, order processing, order shipped, package in transit, package exception, package delivered) 4. Visualize data and "bucket" actionable insights -> INTERNAL VIEW EXAMPLES - "orders received but not picked", "orders picked but not packed, "Label created but not shipped, Packages in transit, Carrier exceptions, Packages in transit beyond customer promise, Packages in transit beyond customer promise, Out for Delivery, Lost Packages, Shipments in transit + "x" days, etc..." EXTERNAL VIEW EXAMPLES - "Orders placed", Orders picked, Orders Packed, Labels created but not shipped, Packages shipped, Packages in Transit, Package exceptions, Packages late, Packages lost, Packages damaged, etc..." 5. Develop CUSTOMER CENTRIC KPIs and assign responsible parties for reviewing dashboards and taking action.

Ever struggle with unpredictable demand and supply constraints? 🤔 I believe Sequential Decision Analytics (SDA) can make a real difference. 📦 Scenario: You’re managing inventory for multiple products. Traditional methods rely on static plans based on fixed forecasts. But what happens when demand spikes unexpectedly or a supplier delays shipments? 🔍 SDA Approach: Instead of building one rigid plan, you create a sequence of decisions that adapt over time. 1️⃣ Capture the State: Gather everything you know—current inventory, pending orders, supplier reliability. 2️⃣ Decision Policy: Decide how much to reorder, whether to reallocate stock, or adjust lead times. This policy doesn’t just react to what’s happening now; it anticipates future changes. 3️⃣ Sequential Planning: Plan each step with the long-term goal in mind. Adjust your strategy as new data arrives, like shifts in demand or supply issues. It’s not about real-time reactions but about making informed, sequential choices. 🔄 Learning and Adaptation: Refine your policy as you learn. If a supplier is consistently late, factor that into future decisions, so your plan gets better with each iteration. 🎯 Objective: Optimize long-term profitability and service levels, not just by minimizing cost in a static model but by balancing risks like stockouts and overstock over time. With SDA, you're not just guessing or reacting; you’re building a resilient, adaptive strategy for your supply chain. What are your thoughts on this framework and approach? 🤔 #OperationsResearch #SupplyChain #InventoryOptimization #SequentialDecisionAnalytics